class_docstr: The LogisticRidgeRegression class is used to fit logistic ridge regression models against one or more labels optimized over a
  provided list of ridge alpha parameters. The optimal ridge alpha value is chosen for each label by minimizing the
  average out of fold log_loss scores.
code: def set_cov_df(
      self, cov_df: pd.DataFrame, add_intercept: bool
  ) -> None:
      self._cov_df = cov_df
      self._std_cov_df = _prepare_covariates(
          cov_df, self._label_df, add_intercept
      )
  
imports: from .logistic_udfs import *
  from .ridge_reduction import RidgeReduction
  from .model_functions import _prepare_labels_and_warn, _prepare_covariates, _check_model, _check_cv, _is_binary
  from nptyping import Float, NDArray
  import pandas
  from pyspark.sql import DataFrame
  from pyspark.sql.functions import pandas_udf, PandasUDFType
  import pyspark.sql.functions
  from typeguard import typechecked
  from typing import Any, Dict, List
  import warnings
  from glow.logging import record_hls_event
label_desc: Use image node_id 6 for calling the LogisticRidgeRegression obj's underlying member method code with example usage: obj.set_cov_df(cov_df, add_intercept) without return types